Details
| Original language | English |
|---|---|
| Article number | 1537066 |
| Journal | Frontiers in Marine Science |
| Volume | 12 |
| Publication status | Published - 28 Apr 2025 |
Abstract
Understanding beach dynamics and the long-term evolution of beach nourishment projects is critical for sustainable coastal management, particularly in the face of rising sea levels and increasingly variable storm climates. This study examines the development of a large-scale sand nourishment (600,000 m³) in the southwestern Baltic Sea over 25 months (October 2021–November 2023) using UAV-derived digital surface models (DSMs) and machine learning (ML). High-frequency, multi-temporal UAV surveys enabled detailed analyses of the development of the nourished beach and dune. Results revealed that the volumetric impact of the 100-year flood in October 2023 was comparable to the cumulative effects of the October 2022–January 2023 storm season. This demonstrates that both episodic extreme events and the cumulative impacts shape the morphological evolution of the nourishment. The study also highlights sediment transport reversals under easterly winds, promoting longer-term stability by retaining sediment within the system. By standardizing volumetric analyses using tools equipped with ML, this research provides actionable insights for adaptive management and establishes a framework for comparable, accurate assessments of nourishment lifetime. In particular, these methods efficiently capture subtle variations in coastline orientation, wave incidence angles, and resulting alongshore beach dynamics, offering valuable insights for optimizing nourishment strategies. These findings underscore the importance of continuous, high-resolution monitoring in developing sustainable strategies for storm-driven erosion and sea level rise.
Keywords
- 100-year flood, co-alignment, machine learning, RTK-UAV, sand nourishments
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Oceanography
- Environmental Science(all)
- Global and Planetary Change
- Agricultural and Biological Sciences(all)
- Aquatic Science
- Environmental Science(all)
- Water Science and Technology
- Environmental Science(all)
- Environmental Science (miscellaneous)
- Engineering(all)
- Ocean Engineering
Sustainable Development Goals
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In: Frontiers in Marine Science, Vol. 12, 1537066, 28.04.2025.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Assessment of sand nourishment dynamics under repeated storm impact supported by machine learning-based analysis of UAV data
AU - Tiede, Jan
AU - Lovell, Joshua Leon
AU - Jordan, Christian
AU - Moghimi, Armin
AU - Schlurmann, Torsten
N1 - Publisher Copyright: Copyright © 2025 Tiede, Lovell, Jordan, Moghimi and Schlurmann.
PY - 2025/4/28
Y1 - 2025/4/28
N2 - Understanding beach dynamics and the long-term evolution of beach nourishment projects is critical for sustainable coastal management, particularly in the face of rising sea levels and increasingly variable storm climates. This study examines the development of a large-scale sand nourishment (600,000 m³) in the southwestern Baltic Sea over 25 months (October 2021–November 2023) using UAV-derived digital surface models (DSMs) and machine learning (ML). High-frequency, multi-temporal UAV surveys enabled detailed analyses of the development of the nourished beach and dune. Results revealed that the volumetric impact of the 100-year flood in October 2023 was comparable to the cumulative effects of the October 2022–January 2023 storm season. This demonstrates that both episodic extreme events and the cumulative impacts shape the morphological evolution of the nourishment. The study also highlights sediment transport reversals under easterly winds, promoting longer-term stability by retaining sediment within the system. By standardizing volumetric analyses using tools equipped with ML, this research provides actionable insights for adaptive management and establishes a framework for comparable, accurate assessments of nourishment lifetime. In particular, these methods efficiently capture subtle variations in coastline orientation, wave incidence angles, and resulting alongshore beach dynamics, offering valuable insights for optimizing nourishment strategies. These findings underscore the importance of continuous, high-resolution monitoring in developing sustainable strategies for storm-driven erosion and sea level rise.
AB - Understanding beach dynamics and the long-term evolution of beach nourishment projects is critical for sustainable coastal management, particularly in the face of rising sea levels and increasingly variable storm climates. This study examines the development of a large-scale sand nourishment (600,000 m³) in the southwestern Baltic Sea over 25 months (October 2021–November 2023) using UAV-derived digital surface models (DSMs) and machine learning (ML). High-frequency, multi-temporal UAV surveys enabled detailed analyses of the development of the nourished beach and dune. Results revealed that the volumetric impact of the 100-year flood in October 2023 was comparable to the cumulative effects of the October 2022–January 2023 storm season. This demonstrates that both episodic extreme events and the cumulative impacts shape the morphological evolution of the nourishment. The study also highlights sediment transport reversals under easterly winds, promoting longer-term stability by retaining sediment within the system. By standardizing volumetric analyses using tools equipped with ML, this research provides actionable insights for adaptive management and establishes a framework for comparable, accurate assessments of nourishment lifetime. In particular, these methods efficiently capture subtle variations in coastline orientation, wave incidence angles, and resulting alongshore beach dynamics, offering valuable insights for optimizing nourishment strategies. These findings underscore the importance of continuous, high-resolution monitoring in developing sustainable strategies for storm-driven erosion and sea level rise.
KW - 100-year flood
KW - co-alignment
KW - machine learning
KW - RTK-UAV
KW - sand nourishments
UR - http://www.scopus.com/inward/record.url?scp=105004764878&partnerID=8YFLogxK
U2 - 10.3389/fmars.2025.1537066
DO - 10.3389/fmars.2025.1537066
M3 - Article
AN - SCOPUS:105004764878
VL - 12
JO - Frontiers in Marine Science
JF - Frontiers in Marine Science
SN - 2296-7745
M1 - 1537066
ER -